LabPiQture™ Overview

A LabPiQture report from ExamOne will contain up to seven years of clinical lab test results. These results are obtained from the vast network of Quest Diagnostics and LabCorp, and are populated with physician-ordered laboratory tests related to preventative care, disease monitoring and diagnostic purposes, with some additional coverage from biometric screenings. Additional medical context returned with the test results are date of service, ordering physician specialty, submitted diagnosis codes (ICD-9/10-CM), and a standardized test type identifier. This data is HIPAA compliant with applicant authorization and meets FCRA requirements.

Study Data

The sample LabPiQture data supplied by ExamOne consists of approximately 72,000 unique life insurance applicants with LP hits. Additional processing of the raw LabPiQture records was necessary given the range of tests and result types returned. We decided to focus on lab tests that are typically ordered during life underwriting. Filtering down to those tests of interest was facilitated by LOINC codes, an international standard for uniquely identifying health measurements. Carriers looking to incorporate LabPiQture will also need to clean and filter the raw data as not all lab results returned are relevant to current underwriting practices, and may require unit standardization.

In this sample data, male records are more likely than female records to have at least one clinical result for the core test groupings identified in Table 2. This is depicted in Figure 1, which also shows that these gender differences are larger at younger issue ages. This is partially explained by Ob/Gyn related tests for younger females, where having an Ob/Gyn related clinical encounter may generate a LabPiQture hit for a female applicant, but does not necessarily result in insurance-relevant tests. Most of the clinical results identified in Table 2 are generated by general/family practice or internal medicine related visits.

Figure 1 also shows that older issue ages have higher prevalence for these clinical tests compared to younger ages (after controlling for gender). Differences are also observed by region, with lower prevalence in the Midwest (not shown). Please note that these patterns relate to individuals with LabPiQture records (i.e. any clinical lab history), and not overall hit rates.

As this sample dataset was populated with both LabPiQture hits and insurance exam lab test results, we could compare the consistency of lab values obtained from these two sources. An example of the graphical approach used is detailed in the appendix below (Figure 3), shown specifically for male total cholesterol.

We generally observe positive association each for the lab tests addressed previously in Table 2. The clinical test result and the insurance exam result also tend to be more similar in value when the time difference between the two is shorter. Blood glucose clinical tests do show more variance relative to the corresponding insurance exam value than most of the other lab tests, which may be related to lower stability of blood glucose measurements over time, different fasting regimes prior to testing, and/or degradation of samples due to glycolysis.

Some insurance-relevant labs that are found infrequently in the clinical histories in this sample include:

• Biometric measurements such as height, weight, and blood pressure: Some results are found for about 2% of hits.
• Cotinine (whether obtained from blood serum, urine, or saliva): Very rare as testing for smoking lacks clinical diagnostic significance.
• GGT: Available on less than 4% of records in this sample; infrequently ordered in clinical settings compared to the rest of the liver function tests.
• Qualitative and semi-quantitative urinalysis (for example, urine creatinine, protein, glucose or albumin): Roughly 7-25% prevalence per test, but requires extra refinement to only capture the exact tests of highest relevance.
• Drug Abuse Screens (Opioids, Marijuana, Cocaine, etc): Present in approximately 3% of records.

In addition, the following labs are found at slightly higher rates, though with strong gender and age patterns:

• PSA: Over 50% of records for males over the age of 45 have at least one result.
• HIV: For females, the highest test prevalence is observed at issue ages under 40 (~35% of records have a related test), and overall, 21% of female records have a test. Males have a less steep trend by age and an overall prevalence of 12%.
• Hepatitis B: Very similar to HIV.
• Hepatitis C: Age and gender differentiation is similar to Hepatitis B, but less extreme. Overall, 20% of female records and 16% of male records have a result.

The consequences of not being able to rely on clinical histories for obtaining these labs which have protective value for insurance underwriting depends on the how LabPiQture results are incorporated into an underwriting workflow as well as any overlapping data sources available (i.e. medical disclosures, additional third-party evidence such as prescription drug records, smoker misrepresentation predictive model output, etc).

The clinical test histories obtained from LabPiQture can complement current risk assessment practices or be used to develop new ones. For example, clinical test results could be used to trigger a specific underwriting path during accelerated underwriting, with the goal of increasing throughput while minimizing additional mortality cost. Under current COVID-19 pandemic conditions where obtaining insurance labs or APSs has come under additional strain, some carriers are using clinical labs in lieu of insurance exams temporarily, or to move the process along more quickly.

The structured nature of the LabPiQture record means that after an initial exploration stage, automating data cleaning and standardization would be fairly straightforward for common tests. This is particularly relevant if the data is consumed downstream by a rules engine or predictive model. Models may need to be calibrated to reflect some of the characteristics for clinical labs. For example, the presence of an HbA1c clinical test on its own may be informative, given that it tends to be ordered for cause, and so any missing value imputation would need to reflect this. Finally, LabPiQture results may contain additional lab tests not traditionally ordered during underwriting but known to be associated with mortality, which creates new opportunities for additional risk segmentation or protective value.

Munich Re’s analysis based on the collaborative study data provided by Exam One demonstrates that LabPiQture returns clinical test results that correspond with life insurance laboratory testing, as well as additional lab information such as other tests not commonly ordered in insurance exams or multiple measurements over time that could expand current risk assessment. Individual carriers should perform a study on their own block of business to assess hit rates and test prevalence as well as the cost-benefit of adopting this additional data source. Munich Re can work with insurers to set up the validation and cost-benefit study, and assist them in incorporating this additional information in their underwriting process.

We also recently validated ExamOne’s HealthPiQture scores, an analytics suite with multiple mortality score components, including a lab mortality score that is derived from LabPiQture clinical histories. Read more for discussion of the effectiveness of this score in segmenting mortality risk.

Appendix

Example: Clinical vs Insurance Exam Result Consistency